A delay and cost balancing protocol for message routing in mobile delay tolerant networks

The increasing pervasiveness of mobile devices with networking capabilities has led to the emergence of Mobile Delay Tolerant Networks (MDTNs). The characteristics of MDTNs, which include frequent and long-term partitions, make message routing a major challenge in these networks. Most of the existing routing protocols either allocate an unlimited number of message copies or use a xed number of message copies to route a message towards its destination. While the first approach unnecessarily oods the network, the rigidity of the second approach makes it ine cient from the viewpoint of message replication. Hence, the question that we address in this paper is: "How to dynamically allocate message copies in order to strike a balance between the delay and cost of message delivery?". We present a novel adaptive multi-step routing protocol for MDTNs. In each routing step, our protocol reasons on the remaining time-tolive of the message in order to allocate the minimum number of copies necessary to achieve a given delivery probability. Experiment results demonstrate that our protocol has a higher delivery ratio and a lower delivery cost compared to the state-of-the-art Spray-and-Wait and Bubble protocols

A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented

An Investigation on the Unwillingness of Nodes to Participate in Mobile Delay Tolerant Network Routing

Message routing in mobile delay tolerant networks inherently relies on the cooperationbetweennodes. Inmostexistingroutingprotocols,theparticipation of nodes in the routing process is taken as granted. However, in reality, nodes can be unwilling to participate. We first show in this paper the impact of the unwillingness of nodes to participate in existing routing protocols through a set of experiments. Results show that in the presence of even a small proportion of nodes that do not forward messages, performance is heavily degraded. We then analyze two major reasons of the unwillingness of nodes to participate, i.e., their rational behavior (also called selfishness) and their wariness of disclosing private mobility information. Our main contribution in this paper is to survey the existing related research works that overcome these two issues. We provide a classification of the existing approaches for protocols that deal with selfish behavior. We then conduct experiments to compare the performance of these strategies for preventing different types of selfish behavior. For protocols that preserve the privacy of users, we classify the existing approaches and provide an analytical comparison of their security guarantees

An investigation on the unwillingness of nodes to participate in mobile delay tolerant network routing

International audienceMessage routing in mobile delay tolerant networks inherently relies on the cooperation between nodes. In most existing routing protocols, the participation of nodes in the routing process is taken as granted. However, in reality, nodes can be unwilling to participate. We first show in this paper the impact of the unwillingness of nodes to participate in existing routing protocols through a set of experiments. Results show that in the presence of even a small proportion of nodes that do not forward messages, performance is heavily degraded. We then analyze two major reasons of the unwillingness of nodes to participate, i.e., their rational behavior (also called selfishness) and their wariness of disclosing private mobility information.Our main contribution in this paper is to survey the existing related research works that overcome these two issues. We provide a classification of the existing approaches for protocols that deal with selfish behavior. We then conduct experiments to compare the performance of these strategies for preventing different types of selfish behaviors. For protocols that preserve the privacy of users, we classify the existing approaches and provide an analytical comparison of their security guarantees

FSF: Applying machine learning techniques to data forwarding in socially selfish Opportunistic Networks

[EN] Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our ¿FriendShip and Acquaintanceship Forwarding¿ (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.This work was partially supported by the "Camilo Batista de Souza/Programa Doutorado-sanduiche no Exterior (PDSE)/Processo 88881.133931/2016-01" and by the Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018, Spain, under Grant RTI2018-096384-B-I00".Souza, C.; Mota, E.; Soares, D.; Manzoni, P.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM.; Hernández-Orallo, E. (2019). FSF: Applying machine learning techniques to data forwarding in socially selfish Opportunistic Networks. Sensors. 19(10):1-26. https://doi.org/10.3390/s19102374S1261910Trifunovic, S., Kouyoumdjieva, S. T., Distl, B., Pajevic, L., Karlsson, G., & Plattner, B. (2017). A Decade of Research in Opportunistic Networks: Challenges, Relevance, and Future Directions. IEEE Communications Magazine, 55(1), 168-173. doi:10.1109/mcom.2017.1500527cmLu, X., Lio, P., & Hui, P. (2016). Distance-Based Opportunistic Mobile Data Offloading. Sensors, 16(6), 878. doi:10.3390/s16060878Zeng, F., Zhao, N., & Li, W. (2017). Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks. Sensors, 17(5), 1109. doi:10.3390/s17051109Khabbaz, M. J., Assi, C. M., & Fawaz, W. F. (2012). Disruption-Tolerant Networking: A Comprehensive Survey on Recent Developments and Persisting Challenges. IEEE Communications Surveys & Tutorials, 14(2), 607-640. doi:10.1109/surv.2011.041911.00093Miao, J., Hasan, O., Mokhtar, S. B., Brunie, L., & Yim, K. (2013). An investigation on the unwillingness of nodes to participate in mobile delay tolerant network routing. International Journal of Information Management, 33(2), 252-262. doi:10.1016/j.ijinfomgt.2012.11.001CRAWDAD Dataset Uoi/Haggle (v. 2016-08-28): Derived from Cambridge/Haggle (v. 2009-05-29)https://crawdad.org/uoi/haggle/20160828Eagle, N., Pentland, A., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274-15278. doi:10.1073/pnas.0900282106Tsai, T.-C., & Chan, H.-H. (2015). NCCU Trace: social-network-aware mobility trace. IEEE Communications Magazine, 53(10), 144-149. doi:10.1109/mcom.2015.7295476Hui, P., Crowcroft, J., & Yoneki, E. (2011). BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks. IEEE Transactions on Mobile Computing, 10(11), 1576-1589. doi:10.1109/tmc.2010.246Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), 19-20. doi:10.1145/961268.961272Cao, Y., & Sun, Z. (2013). Routing in Delay/Disruption Tolerant Networks: A Taxonomy, Survey and Challenges. IEEE Communications Surveys & Tutorials, 15(2), 654-677. doi:10.1109/surv.2012.042512.00053Zhu, Y., Xu, B., Shi, X., & Wang, Y. (2013). A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects. IEEE Communications Surveys & Tutorials, 15(1), 387-401. doi:10.1109/surv.2012.032612.00004Shah, R. C., Roy, S., Jain, S., & Brunette, W. (2003). Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Networks, 1(2-3), 215-233. doi:10.1016/s1570-8705(03)00003-9Burns, B., Brock, O., & Levine, B. N. (2008). MORA routing and capacity building in disruption-tolerant networks. Ad Hoc Networks, 6(4), 600-620. doi:10.1016/j.adhoc.2007.05.002Shaghaghian, S., & Coates, M. (2015). Optimal Forwarding in Opportunistic Delay Tolerant Networks With Meeting Rate Estimations. IEEE Transactions on Signal and Information Processing over Networks, 1(2), 104-116. doi:10.1109/tsipn.2015.2452811Li, L., Qin, Y., & Zhong, X. (2016). A Novel Routing Scheme for Resource-Constraint Opportunistic Networks: A Cooperative Multiplayer Bargaining Game Approach. IEEE Transactions on Vehicular Technology, 65(8), 6547-6561. doi:10.1109/tvt.2015.2476703Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking. ACM SIGPLAN Notices, 37(10), 96-107. doi:10.1145/605432.605408Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2008). Efficient Routing in Intermittently Connected Mobile Networks: The Single-Copy Case. IEEE/ACM Transactions on Networking, 16(1), 63-76. doi:10.1109/tnet.2007.897962Zhang, L., Wang, X., Lu, J., Ren, M., Duan, Z., & Cai, Z. (2014). A novel contact prediction-based routing scheme for DTNs. Transactions on Emerging Telecommunications Technologies, 28(1), e2889. doi:10.1002/ett.2889Okasha, S. (2005). Altruism, Group Selection and Correlated Interaction. The British Journal for the Philosophy of Science, 56(4), 703-725. doi:10.1093/bjps/axi143Hernandez-Orallo, E., Olmos, M. D. S., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2015). CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes. IEEE Transactions on Mobile Computing, 14(6), 1162-1175. doi:10.1109/tmc.2014.234362

ADDRESSING SELFISHNESS IN THE DESIGN OF COOPERATIVE SYSTEMS

I sistemi distribuiti cooperativi, tra cui in particolare i sistemi peer-to-peer, sono oggi alla base di applicazioni Internet di larga diffusione come file-sharing e media streaming, nonch\ue9 di tecnologie emergenti quali Blockchain e l'Internet of Things. Uno dei fattori chiave per il successo di un sistema cooperativo \ue8 che i nodi che vi partecipano mettano a disposizione della comunit\ue0 una parte delle proprie risorse (es. capacit\ue0 di calcolo, banda, spazio disco). Alcuni nodi, poich\ue9 controllati da agenti autonomi e indipendenti, potrebbero tuttavia agire egoisticamente e scegliere di non condividere alcuna risorsa, spinti dall'obiettivo di massimizzare la propria utilit\ue0 anche se a danno delle prestazioni dell'intero sistema. Affrontare l'egoismo dei nodi rappresenta dunque un'attivit\ue0 imprescindibile per lo sviluppo di un sistema cooperativo affidabile e performante. Nonostante il grande numero di tecniche ed approcci presenti in letteratura, tale attivit\ue0 richiede elaborazioni complesse, manuali e laboriose, nonch\ue9 conoscenze approfondite in vari domini di applicazione. Obiettivo di questa tesi \ue8 di fornire strumenti sia pratici che teorici per semplificare lo studio e il contrasto dei comportamenti egoistici nei sistemi cooperativi. Il primo contributo, basato su un'analisi esaustiva dello stato dell'arte sull'egoismo in sistemi distribuiti, presenta un framework di classificazione finalizzato all'identificazione e comprensione dei comportamenti egoistici pi\uf9 importanti su cui concentrarsi durante la progettazione di un sistema cooperativo. Come secondo contributo, presentiamo RACOON, un framework per la progettazione e configurazione di sistemi cooperativi resilienti all'egoismo dei nodi. L'obiettivo di RACOON \ue8 di semplificare tali attivit\ue0 fornendo una metodologia generale e semi-automatica, capace di integrare in un dato sistema pratici meccanismi di incentivo alla cooperazione, attentamente calibrati in modo da raggiungere gli obiettivi di resilienza e performance desiderati. A tal fine, RACOON impiega sia strumenti analitici appartenenti alla teoria dei giochi che metodi simulativi, che vengono utilizzati per fare previsioni sul comportamento del sistema in presenza di nodi egoisti. In questa tesi presentiamo inoltre una versione estesa del framework, chiamata RACOON++, sviluppata per migliorare l'accuratezza, flessibilit\ue0 e usabilit\ue0 del framework originale. Infine, come ultimo contributo del lavoro di tesi, presentiamo SEINE, un framework per la rapida modellazione e analisi sperimentale di vari tipi di comportamenti egoistici in un dato sistema cooperativo. Il framework \ue8 basato su un nuovo linguaggio specifico di dominio (SEINE-L) sviluppato per la descrizione degli scenari di egoismo da analizzare. SEINE fornisce inoltre supporto semi-automatico per l'implementazione e lo studio di tali scenari in un simulatore di sistemi distribuiti selezionato dallo stato dell'arte.Cooperative distributed systems, particularly peer-to-peer systems, are the basis of several mainstream Internet applications (e.g., file-sharing, media streaming) and the key enablers of new and emerging technologies, including blockchain and the Internet of Things. Essential to the success of cooperative systems is that nodes are willing to cooperate with each other by sharing part of their resources, e.g., network bandwidth, CPU capability, storage space. However, as nodes are autonomous entities, they may be tempted to behave in a selfish manner by not contributing their fair share, potentially causing system performance degradation and instability. Addressing selfish nodes is, therefore, key to building efficient and reliable cooperative systems. Yet, it is a challenging task, as current techniques for analysing selfishness and designing effective countermeasures remain manual and time-consuming, requiring multi-domain expertise. In this thesis, we aim to provide practical and conceptual tools to help system designers in dealing with selfish nodes. First, based on a comprehensive survey of existing work on selfishness, we develop a classification framework to identify and understand the most important selfish behaviours to focus on when designing a cooperative system. Second, we propose RACOON, a unifying framework for the selfishness-aware design and configuration of cooperative systems. RACOON provides a semi-automatic methodology to integrate a given system with practical and finely tuned mechanisms to meet specified resilience and performance objectives, using game theory and simulations to predict the behaviour of the system when subjected to selfish nodes. An extension of the framework (RACOON++) is also proposed to improve the accuracy, flexibility, and usability of RACOON. Finally, we propose SEINE, a framework for fast modelling and evaluation of various types of selfish behaviour in a given cooperative system. SEINE relies on a domain-specific language for describing the selfishness scenario to evaluate and provides semi-automatic support for its implementation and study in a state-of-the-art simulator.Les syst\ue8mes distribu\ue9s collaboratifs, en particulier les syst\ue8mes pair-\ue0-pair, forment l\u2019infrastructure sous-jacente de nombreuses applications Internet, certaines parmi les plus populaires (ex\ua0: partage de fichiers, streaming multim\ue9dia). Ils se situent \ue9galement \ue0 la base d\u2019un ensemble de technologies \ue9mergentes telles que la blockchain et l\u2019Internet des Objets. Le succ\ue8s de ces syst\ue8mes repose sur la contribution volontaire, de la part des n\u153uds participants, aux ressources partag\ue9es (ex : bande passante r\ue9seau, puissance de calcul, stockage de donn\ue9es). Or ces n\u153uds sont des entit\ue9s autonomes qui peuvent consid\ue9rer comme plus avantageux de se comporter de mani\ue8re \ue9go\uefste, c\u2019est-\ue0- dire de refuser de collaborer. De tels comportements peuvent fortement impacter les performances et la stabilit\ue9 op\ue9rationnelles du syst\ue8me cible. Prendre en compte et pr\ue9venir les comportements \ue9go\uefstes des n\u153uds est donc essentiel pour garantir l\u2019efficacit\ue9 et la fiabilit\ue9 des syst\ue8mes coop\ue9ratifs. Cependant, cela exige du d\ue9veloppeur, en d\ue9pit de la grande quantit\ue9 de techniques et d\u2019approches propos\ue9es dans la litt\ue9rature, des connaissances multisectorielles approfondies. L'objectif de cette th\ue8se est de concevoir et \ue9tudier de nouveaux outils th\ue9oriques et pratiques pour aider les concepteurs de syst\ue8mes distribu\ue9s collaboratifs \ue0 faire face \ue0 des n\u153uds \ue9go\uefstes. La premi\ue8re contribution, bas\ue9e sur une analyse exhaustive de la litt\ue9rature sur les comportements \ue9go\uefstes dans les syst\ue8mes distribu\ue9s, propose un mod\ue8le de classification pour identifier et analyser les comportements \ue9go\uefstes les plus importants sur lesquels il est important de se concentrer lors de la conception d'un syst\ue8me coop\ue9ratif. Dans la deuxi\ue8me contribution, nous proposons RACOON, un framework pour la conception et la configuration de syst\ue8mes coop\ue9ratifs r\ue9silients aux comportements \ue9go\uefstes. Outre un ensemble de m\ue9canismes d'incitation \ue0 la coop\ue9ration, RACOON fournit une m\ue9thodologie semi-automatique d\u2019int\ue9gration et de calibration de ces m\ue9canismes de mani\ue8re \ue0 garantir le niveau de performance souhait\ue9. RACOON s\u2019appuie sur une analyse du syst\ue8me cible fond\ue9e sur la th\ue9orie des jeux et sur des simulations pour pr\ue9dire l\u2019existence de n\u153uds \ue9go\uefstes dans le syst\ue8me. RACOON a \ue9t\ue9 \ue9tendu en un deuxi\ue8me framework, RACOON++. Plus pr\ue9cis, plus flexible, RACOON++ offre \ue9galement une plus grande facilit\ue9 d'utilisation. Une derni\ue8re contribution, SEINE, propose un framework pour la mod\ue9lisation et l'analyse des diff\ue9rents types de comportements \ue9go\uefstes dans un syst\ue8me coop\ue9ratif. Bas\ue9 sur un langage d\ue9di\ue9, d\ue9velopp\ue9 pour d\ue9crire les sc\ue9narios de comportement \ue9go\uefstes, SEINE fournit un support semi-automatique pour la mise en \u153uvre et l'\ue9tude de ces sc\ue9narios dans un simulateur choisi sur la base de l\u2019\ue9tat de l\u2019art (PeerSim)